Your AI Task Force Needs an Exit Strategy

Aug 30

By Claire L. Brady, EdD

This post is part of a series exploring Achieving the Dream’s Creating the AI-Enabled Community College framework through a leadership lens. While ATD is focused on community colleges, the report’s core questions about strategy, governance, culture, workforce readiness, professional learning, and student success apply across institution types. Throughout the series, I’ll examine what its eight action areas look like in practice and what it takes to move from scattered AI activity toward intentional, sustainable institutional change.

Most AI task forces are created with an ending in mind. They receive a charge, spend a year or two studying the issues, develop recommendations, write guidelines, and submit a final report. Leadership thanks the members for their service, the group disbands, and everyone returns to their regular responsibilities.

Then the real governance work begins.

New tools are proposed. Policies encounter situations no one anticipated. Faculty and staff need help interpreting guidance. A pilot expands into a larger implementation. Students raise concerns about privacy or transparency. Someone needs to assess whether an AI system is producing different outcomes across student populations.

The task force may have written thoughtful recommendations, but who now has the authority to act on them?

Policies Are Not Governance

Achieving the Dream’s second action area calls on institutions to establish ethical AI governance that addresses privacy, academic integrity, fairness, transparency, bias, equitable access, and continuous human oversight. Those responsibilities cannot be resolved through a single policy or completed by a temporary committee.

Policies provide direction. Governance determines how that direction is interpreted, applied, monitored, and revised as circumstances change.

This distinction matters because AI will continue to present situations institutions did not anticipate when their original guidelines were written. A department may want to use an AI tool to prioritize prospective students. A faculty member may use AI to provide feedback or assign grades. An advising platform may begin processing more sensitive student data than originally expected. A chatbot that looked promising at launch may prove confusing or inaccessible in practice.

Someone has to decide what happens next.

Build Governance That Can Outlast Its Founders

In AI with Intention, I argue that task forces are valuable for getting started, but they are not durable governance. They bring together expertise, create momentum, and help institutions respond to an urgent moment. Their limitations become clear after they disband, when the people who understood why decisions were made are no longer gathered around the same table.

Durable governance preserves more than policies. It preserves institutional memory, decision-making authority, accountability, and the capacity to learn.

That requires an intentional transition from temporary work to permanent ownership. Institutions need clarity about who can approve, pause, revise, or stop an AI implementation. They need established pathways for concerns to be raised and reviewed. They need connections among AI governance, faculty governance, procurement, privacy, accessibility, legal counsel, academic affairs, student affairs, and information technology.

They also need to resource the work. Governance that depends entirely on volunteers squeezing meetings and policy reviews around full-time jobs will eventually weaken, no matter how committed the original members may be.

Governance Must Continue After Approval

One of the most common mistakes institutions make is treating governance as a front-end approval process. A tool is reviewed, risks are discussed, permission is granted, and the work is considered complete.

But implementations change once they encounter real students, employees, data, and institutional processes. Tools may be used differently than originally proposed. Equity gaps may emerge. Workloads may increase rather than decrease. Students may struggle to access human support. A technically successful implementation may quietly move the institution away from its values.

Durable governance monitors those outcomes and has the authority to intervene. It also learns. Decisions create precedents, failures reveal weaknesses, and new situations require policies to evolve. Ethical governance is not a static set of rules. It is an ongoing institutional practice of collective judgment.

That is why every AI task force needs an exit strategy before it finishes its work.

The exit strategy should answer a few practical questions: Where will permanent authority live? Who will coordinate the work? How will decisions be documented? Which issues can be handled within individual units, and which require institutional review? How will faculty, staff, students, and relevant experts remain involved? How often will policies and processes be revisited?

The goal is not to keep the original task force meeting forever. It is to ensure that the responsibilities it surfaced do not disappear when the group does.

A Question for Leaders

When your current AI task force completes its charge, who will inherit its authority, knowledge, and responsibilities?

If the answer is unclear, the task force’s most important remaining deliverable may not be another policy. It may be the governance structure that comes next.